There are many Internet-based or web-based social networks that interconnect users and allow them to interact. To facilitate such interactions, each user builds their user profile to establish a network identity. Having a network identity that properly reflects the user's characteristics is important. A user's profile allows him or her to connect to other users who are already friends, or users whom the user does not yet know. Once connected, the users can interact over common interests, values, needs or goals.
Improperly constructed user profiles present obstructions to meaningful user interactions on social networks. Inaccurate profiles often occur in social networks that allow users to build their profiles on self-reported data only. Users can easily provide an inaccurate portrayal of themselves in self-reports. In other words, users are likely to game the system to attempt to look better or different than their actual selves.
Poorly constructed user profiles present subsequent system challenges, as they frequently form the basis for computer-generated matching recommendations or other suggestions. Matching recommendations can be for individual interactions as in the case of dating matches between two users. In other cases, the suggestions can be for certain activities or group interactions, such as new community formation among a group of users. Unfortunately, when the recommendations and suggestions are based on improper user profiles, the social value inherent in the social network cannot be unlocked. The matches and suggestions may even prejudice the users against future reliance on social network suggestions for matches with other users or groups that the user does not already know in the real world.
In order to make user profiles more accurate, some prior art suggests using network behaviors for profiling. For example, U.S. Pat. No. 8,156,064 to Brown discusses collecting user network behaviors to obtain more correct user profiles. Specifically, Brown teaches profiling with respect to one or more domains, and associating network behaviors with the one or more domains by corresponding scale factors. The scale factors are based on a relevance of the observed network behavior to the domain used in the profiling. Furthermore, the scale factors are refined based on user ratings obtained from user inputs.
Brown's teachings help to eliminate many challenges faced by self-reported user profiling and the resulting inaccurate profile matches. However, numerous problems remain. The lives of users are dynamic and many events have the power to change them in major ways. These changes, some of which are large, discontinuous and may even involve complete shifts in outlook are not generally predictable. However, it would be very desirable for user profiles to change dynamically with the users to reflect the users' changed characteristics.
There are presently no viable systems or methods to specifically track events that generate user profile altering experiences. Furthermore, no solutions exist to properly contextualize events and to treat event-based changes in user profiles.